A Benchmark Dataset and Evaluation for Non-Lambertian and Uncalibrated Photometric Stereo

Abstract

Classic photometric stereo is often extended to deal with real-world materials and work with unknown lighting conditions for practicability. To quantitatively evaluate non-Lambertian and uncalibrated photometric stereo, a photometric stereo image dataset containing objects of various shapes with complex reflectance properties and high-quality ground truth normals is still missing. In this paper, we introduce the ‘<italic>DiLiGenT</italic>’ dataset with calibrated <underline>Di</underline>rectional <underline>Li</underline>ghtings, objects of <underline>Gen</underline>eral reflectance with different shininess, and ‘ground <underline>T</underline>ruth’ normals from high-precision laser scanning. We use our dataset to quantitatively evaluate state-of-the-art photometric stereo methods for general materials and unknown lighting conditions, selected from a newly proposed photometric stereo taxonomy emphasizing non-Lambertian and uncalibrated methods. The dataset and evaluation results are made publicly available, and we hope it can serve as a benchmark platform that inspires future research.